Journal
ANALYTICA CHIMICA ACTA
Volume 667, Issue 1-2, Pages 14-32Publisher
ELSEVIER
DOI: 10.1016/j.aca.2010.03.048
Keywords
Near-infrared spectroscopy; Chemometrics; Wavelength; Variable selection
Categories
Funding
- NSFC [6091079]
- Chinese 863 Program [2008AA10Z208, 2008AA10Z204]
- Postdoctoral Foundation of China [20070411024, 0601003C]
- talent foundation of Jiangsu University
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Near-infrared (NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields, such as the petrochemical, pharmaceutical, environmental, clinical, agricultural, food and biomedical sectors during the past 15 years. A NIR spectrum of a sample is typically measured by modern scanning instruments at hundreds of equally spaced wavelengths. The large number of spectral variables in most data sets encountered in NIR spectral chemometrics often renders the prediction of a dependent variable unreliable. Recently, considerable effort has been directed towards developing and evaluating different procedures that objectively identify variables which contribute useful information and/or eliminate variables containing mostly noise. This review focuses on the variable selection methods in NIR spectroscopy. Selection methods include some classical approaches, such as manual approach (knowledge based selection), Univariate and Sequential selection methods; sophisticated methods such as successive projections algorithm (SPA) and uninformative variable elimination (UVE), elaborate search-based strategies such as simulated annealing (SA), artificial neural networks (ANN) and genetic algorithms (GAs) and interval base algorithms such as interval partial least squares (iPLS), windows PIS and iterative PLS. Wavelength selection with B-spline, Kalman filtering, Fisher's weights and Bayesian are also mentioned. Finally, the websites of some variable selection software and toolboxes for non-commercial use are given. (C) 2010 Elsevier B.V. All rights reserved.
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